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Design of a monitoring plan is a process (Figure 5.1) that will ideally lead you through problem identification to development of key questions, a rigorous sampling design, and analyses that can assign probabilities to observed trends.

Finalizing a plan designed as an outcome of this process is a precursor to initiation of data collection. This is probably the single most important step in the monitoring plan. Once you have decided on the design for the monitoring plan, and begun collecting data, there is strong resistance to changing the plan because many changes will render the data collected thus far of less value. So design it correctly from the outset to minimize the need for changes later.

Broad Scale Problem Specific Problem Identification

Specific Problem

Preliminary Predictions

Decision with Existing Data

Decision Not Made with Existing Data;

Monitoring Needed

Prioritize Objectives

Decision on Types of Data

Data Collection

Analysis and Recommendation Predictions Based

on the Decision Monitoring Study Adjust Predictions

No Decision; No New Data

Decision Based on Adjusted Data No New Data

Collected

Figure 5.1 The inventorying and monitoring process. (Redrafted from Jones 1986.)

ArTiCuLATiNg QueSTiONS TO Be ANSWereD

It is important to view monitoring as comparable in many ways to conducting a scientific investigation. The first step in the process is to develop a conceptual frame-work for our current understanding of the system, complete with literature citations to support assumptions. Clearly no monitoring program will have all of the informa-tion needed to completely develop a conceptual model for the system under consid-eration. Available information will have to be extracted from the literature, from other systems, and from expert opinion. Nonetheless, the conceptual model needs to be developed in order to identify the key gaps in our knowledge and allow a clear articulation of the most pertinent questions (Figure 5.2).

As you develop the monitoring plan you should pay particular attention to some terms that are commonly used to define the problem and the approach. Within the context of land management and biodiversity conservation, these terms might guide you to the kind of monitoring design that you will choose to use.

These terms relate to the experimental design:

Cause and effect—Will you be able to infer the cause for observed changes?

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Association or relationship—Will you be able to detect associations

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between pairs of variables such as populations and changes in area of a habitat type?

Trend or pattern—Will patterns over space and/or time be apparent?

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Observation or detection—What constitutes having “observed’ an individual?

• ambrosia, one of many species identified as important within San Diego’s Multiple Species Conservation Program. (Redrafted from Hierl, L.A., J. Franklin, D.H. Deutschman, and H.M. Regan. 2007. Developing Conceptual Models to Improve the Biological Monitoring Plan for San Diego’s Multiple Species Conservation Program. Department of Biology, San Diego State University, and California Department of Fish and Game, Sacramento, CA.) The goal for managers is to maintain 90% of the base population. Drivers presumably influencing population persistence are highlighted.

Designing a Monitoring Plan 81 These terms relate to the response variable that you will measure to assess one of the above:

Occurrence—Was the species present, absent, or simply not detected?

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Relative abundance—Did you observe more individuals in one place or

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time than another?

Abundance—How many individuals per hectare (or square kilometer) are

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estimated to be present?

Fitness—Is the species surviving or reproducing better in one place or time

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than another?

These terms relate to the scope of inference for the effort:

Stand, harvest unit, field, pasture, project, farm, district, watershed, forest,

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region—Defines the grain and extent of the spatial scale of the potential management effects.

Home range, subpopulation, geographic range, stock, clone—Defines the

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grain and spatial extent associated with the focal species.

Frequency of management or exogenous disturbances affecting the

sys-•

tem—Helps define the sampling interval.

Return interval between disturbances or other events likely to effect

popu-•

lations of the focal species—Helps define the duration of the monitoring framework.

Disturbance intensity or the degree of change in biomass or other aspects of

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the system as a function of management or exogenous disturbances—Helps understand how effect sizes should be defined and hence the sampling inten-sity sufficient to detect trends or differences.

Once you have articulated questions based on the conceptual model for the sys-tem, then you should use terms from each of the groups above to further define the monitoring plan. Detail and focus are important aspects of a well-designed monitoring system. Use of vague or unclear terms, broad questions, or unclear spatial and temporal extents will increase the risk that the data collected will not adequately address the key questions at scales that are meaningful. Further, clearly articulated questions not only ensure that data collected are adequate to address specific key knowledge gaps or assumptions, but they also provide the basis for identifying thresholds or trigger points that initiate a new set of manage-ment actions.

If the above terms are considered when the monitoring plan is being designed, and trigger points for management action are described clearly prior to monitoring, then it should be apparent that the universe of questions that could be addressed by monitoring is very broad. Of course, your challenge is to identify the key questions that address the key processes and states in an efficient and coordinated manner over space and time. Given a conceptual model developed for a system, there is a range of

questions that could be addressed through monitoring. Prioritization of these ques-tions allows the manager to focus time and money on the key quesques-tions.

iNVeNTOrY, MONiTOriNg, AND reSeArCH

The questions of concern may be addressed using inventory, monitoring, or research approaches (Elzinga et al. 1998). Inventory is an extensive point-in-time survey to determine the presence/absence, location, or condition of a biotic or abiotic resource.

Monitoring is a collection and analysis of repeated observations or measurements to evaluate changes in condition and progress toward meeting a management objec-tive. Detecting a trend may trigger a management action. Research has the objective of understanding ecological processes or in some cases determining the cause of changes observed by monitoring. Research is generally defined as the systematic collection of data that produces new knowledge or relationships, and usually involves an experimental approach, in which a hypothesis concerning the probable cause of an observation is tested in situations with and without the specified cause. Some biologists make a strong case that the difference between monitoring and research is subtle and that monitoring should also be based on testable hypotheses. Nonetheless, these three approaches to gaining information are highly complementary and not really very discrete. And all three approaches are needed to effectively manage an area without unnecessary negative effects.

Are DATA ALreADY AVAiLABLe?

You may already have some data that have been collected previously or from a dif-ferent area. Can you use these data? Should you? What constitutes adequate data already in hand, or how do we know when data are adequate to address a question?

Well, that depends on the question! For example, if we want to be 90% sure that a species does not occur in a patch or other area to be managed in some manner in the next year, how many samples are required to reach that level of confidence?

Developing a relationship between the amount of effort expended and the probability of detecting species “x” in a patch can provide insight into the level of effort needed to detect a species 90% of the time when it indeed does occur in the patch. This requires multiple patches and multiple samples per patch over time to place confi-dence intervals on probabilities (Figure 5.3). Where multiple species are the focus of monitoring, a species-area chart can be quite helpful. For example in Figure 5.4, sampling an area less than 7 hectares in size is not likely to result in a representative list of species for the site.

These sorts of questions require quite different data than would be required to answer the question: What are the effects of management “x” on species “y”? Note that the term effect is used in this example, so the experimental design is ideally in the form of a manipulative experiment (Romesburg 1981). In this case, we would want to have both pre- and post-treatment data collected on a sample of patches that do and do not receive treatment. In the following example, two of the treatments clearly had an effect on the abundance of white-crowned sparrows in managed stands in Oregon (Figure 5.5). Results such as these are based on specific questions.

Designing a Monitoring Plan 83

1 0.8 0.6

Probability of Detection at 10 Points 0.4 0.2

1 1 2 3 4 5

Number of Visits

6 7 8 9 10

Figure 5.3 A hypothetical cumulative probability of detection. Note that with increasing sampling effort, the probability of detecting a species increases to a plateau at about 90% with nine visits. Hence, future efforts at detecting this species should include at least nine visits.

Clearly more visits are needed to detect rare species than common species.

20 15

Number of Species Detected 10

5

1 1 2 3 4 5

Area of Sampling Effort (hectares) Number of Species

6 7 8 9 10

Figure 5.4 A hypothetical species-area curve for one patch type. Note that when an asymptote is reached then sampling an area of that size is most likely to capture the most species, until a new patch type is reached, then an abrupt increase in species maybe noted.

Control Gaps

Two-story Clearcut

35 40 45 50

30 25 20 15 10 5

0 Pretreatment 1-year

post-treatment 2-year post-treatment

Birds Observed per 5 ha

Figure 5.5 Change in white-crowned sparrow detections following silvicultural treat-ments illustrating cause and effect monitoring results. (Redrafted from Chambers, C.L., W.C. McComb, and J.C. Tappeiner. 1999. Ecological Applications 9:171–185; inset photo by Laura Erickson, used with permission.)

It is the development of the question that is important, and the question should evolve from the conceptual model of the system. Clearly, the development of a conceptual model to describe the system states, processes, and stressors should be based to the degree possible on data. So although currently available data are valuable, they must address the question of interest in a manner that is consistent with the conceptual model. It is important to recognize that not all data are equal. Consider the follow-ing questions when evaluatfollow-ing the adequacy of a data set to address a question or to develop a conceptual framework:

1. Are samples independent? That is, are observations in the data set repre-senting management units to which a treatment has been applied? Using a forest example, taking 10 samples of densities of an invasive species from one patch is not the same as taking one sample from 10 patches (Hurlbert 1984). In the former example, the samples are subsamples of one treatment area; in the latter, there is one sample in each of 10 repli-cate units. If the species under consideration has a home range that is less than the patch size, then the patches are reasonably independent samples.

If the species under consideration has a home range that spans numerous patches, then the selection of patches to sample should be based on ensur-ing, to the degree possible, that one animal is unlikely to use more than one managed patch.

2. How were the data collected? What sources of variability in the data may be caused by the sampling methodology (e.g., observer bias, inconsistencies in methods, etc.)? If sample variability is too high because of sampling error, then the ability to detect differences or trends will decrease. Further if the samples taken are biased, then the resulting conclusions will be biased, and decisions made based on those conclusions may be inappropriate.

3. Were sites selected randomly? If not, then there may be (likely is) bias intro-duced into the data that should raise doubts in the minds of the scientists, managers, and stakeholders with regard to the accuracy of the resulting relationships or differences.

4.What effect size is reasonable? Even a well-designed study may simply not have the sample size adequate to detect a difference or relationship that is real simply because the study was constrained by resources, rare responses, or other factors that increase the sample variance and decrease the effect size that can be detected. Again, how this is dealt with depends on the question being asked. Which is more important, to detect a rela-tionship that is real or to say that there is no relarela-tionship when there really isn’t? In many instances, where monitoring is designed to detect an effect of a management action, the former is more important. In that case, the alpha level used to detect differences or trends may be increased (from, say, 0.05 to 0.10), but you will be more likely to say a relationship is real when it is really not. Alternatively, you may want to use Bayesian analysis or meta-analysis to examine the data and see if these techniques shed light on your question. See Chapter 11 for a more in-depth discussion of these analytical techniques.

Designing a Monitoring Plan 85 5.What is the scope of inference? From what area were samples selected?

Over what time period? Are the results of the work likely to be applicable to your area? As the differences in the conditions under which the data were collected increase compared to the conditions in your area of interest, the less confidence you should have in applying the results in your context.

If, after considering the above factors, you feel that the data can be used to reliably identify known from unknown states and processes in the conceptual model, then you should have a better idea where the model relies heavily on assumptions, weak data, or expert opinion. These portions of the conceptual model should rise to the top during identification of the question that monitoring should be designed to address.

Provided that the cautions indicated above are explored, it is reasonable and cor-rect to use data that are already available to inform and focus the questions to be asked by a monitoring plan. Existing data are commonly used to address questions. For instance, Sauer et al. (2001) provided a credibility index that flags imprecise, small sample size, or otherwise questionable results. Yellow-billed cuckoos have shown a significant decline in southern New England over the past 34 years (Figure 5.6), but the analysis raises a flag with regards to credibility because of a deficiency in the data associated with low abundance (<1.0 bird/route). Further, an examination of the data would indicate that the one estimate in 1966 may be an outlier and may have an overriding effect on the results. In this example, it would be useful to delete the 1966 data and rerun the analyses to determine if the relationship still holds.

8 7 6 5 4 3 2 1

19660 1971 1976 1981 1986

Year

1991 1996

Count

Figure 5.6 Trends in abundance of yellow-billed cuckoos over its geographic range, 1966–1996. (Redrafted from Sauer, J.R., J.E. Hines, and J. Fallon. 2001. The North American Breeding Bird Survey, Results and Analysis 1966–2000. Version 2001.2, USGS Patuxent Wildlife Research Center, Laurel, MD.) Note that data summaries are also available for smaller areas such as ecoregions and states. These data may address a portion of a conceptual model and be valuable in designing a monitoring plan.

Regardless of the outcome of this subsequent analysis, the data will likely prove useful when developing a conceptual model of population change for the species.

When the proper precautions are taken, such data may allow the manager to focus on more specific questions with regard to a monitoring plan. For instance, within the geographic range of the species, what factors may be causing the predicted declines?

The BBS data reveal that the species is not predicted to have declined uniformly over its range (Figure 5.7). This information may provide an opportunity to develop hypotheses regarding the factors causing the declines.

Recorded trends in abundance for eastern towhees (Figure 5.8) provide another clear example. In this case, declines are apparent throughout the entire northeast-ern United States. Based on our knowledge of changes in land use in the north-east and the association of this species with early successional scrub vegetation, we would hypothesize that the declines are a direct result of the regrowth of the eastern hardwood forests and subsequent loss of shrub-dominated vegetation. Indeed, monitoring the reproductive success of the species prior to and following vegeta-tion management designed to restore shrub vegetavegeta-tion might allow detecvegeta-tion of a cause-and-effect relationship that would lead to a change in monitoring for the spe-cies over the northeastern portion of its range. If changes in abundance were related to changes in land cover (and not to parasitism by brown-headed cowbirds or other effects), then an alternative monitoring framework could be developed. Infrequent monitoring of populations with frequent monitoring of the availability of the habi-tat elements important to the species (shrub cover) may be adequate to understand

Percent Change per Year Less than –1.5 –1.5 to –0.25 –0.25 to +0.25 +0.25 to +1.5 Greater than +1.5

Figure 5.7 (A color version of this figure follows page 144.) Changes in abundance of yellow-billed cuckoos over time varies from one portion of its geographic range to another.

(From Sauer, J.R., J.E. Hines, and J. Fallon. 2001. The North American Breeding Bird Survey, Results and Analysis 1966–2000. Version 2001.2, USGS Patuxent Wildlife Research Center, Laurel, MD.) Care should be given to ensure that the data relate to the scope of inference of the monitoring plan that is being developed. Local trends may be informative, while regional trends may not; if your area of interest were in Louisiana, then national trends would be misleading.

Designing a Monitoring Plan 87

the opportunities for population recovery (or continued decline). This reveals the potential benefits of applying previously generated data. Generally, costs associated with monitoring habitat availability are less than costs associated with monitoring populations or population fitness, so if a cause-and-effect relationship were detected between vegetation and bird populations, then managers could see considerable savings in their monitoring program.

In summary, although there are general guidelines with regards to what constitutes reliable data, adequate data for one question may be inadequate for another. Use of exist-ing data and an understandexist-ing of data quality can allow development of a conceptual model where states, processes, and stressors can be identified with varying levels of confi-dence. Those factors that are based on assumptions or weak data and which seem quite likely to be influencing the ability to understand management effects should become the focus when developing the questions to be answered by the monitoring plan.

TYPeS OF MONiTOriNg DeSigNS

Before we provide examples of the types of monitoring designs, recall that there are several main points consistent among all designs:

1. Are the samples statistically and biologically independent?

2. How were the data collected? What sources of variability in the data may be caused by the sampling methodology (e.g., observer bias, inconsistencies in

Less than –1.5 –0.25 to +0.25 –1.5 to –0.25 + 0.25 to +1.5 Greater than +1.5 Percent Change per Year

Figure 5.8 (A color version of this figure follows page 144.) Predicted changes in abun-dance of eastern towhees over its geographic range, 1966–1996. (From Sauer, J.R., J.E. Hines,

Figure 5.8 (A color version of this figure follows page 144.) Predicted changes in abun-dance of eastern towhees over its geographic range, 1966–1996. (From Sauer, J.R., J.E. Hines,